Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	128,215
2	South Dakota	122,437
3	Rhode Island	109,458
4	Utah	108,300
5	Tennessee	104,762
6	Arizona	104,751
7	Wisconsin	101,833
8	Iowa	101,483
9	Nebraska	98,852
10	Oklahoma	98,779
11	Arkansas	98,248
12	Kansas	95,968
13	Alabama	93,991
14	Indiana	93,802
15	Mississippi	92,638
16	Idaho	91,373
17	Nevada	90,658
18	Wyoming	89,945
19	Illinois	89,269
20	Montana	88,087
21	South Carolina	86,607
22	Louisiana	86,386
23	California	84,561
24	Georgia	83,679
25	Texas	83,269
26	New Mexico	83,244
27	Kentucky	82,508
28	Minnesota	82,025
29	Missouri	80,631
30	Delaware	80,609
31	Florida	80,413
32	New Jersey	78,848
33	Ohio	76,916
34	Massachusetts	76,376
35	Alaska	73,940
36	New York	73,461
37	North Carolina	72,783
38	Connecticut	71,229
39	Colorado	69,352
40	West Virginia	67,753
41	Pennsylvania	66,545
42	Michigan	61,202
43	Virginia	59,473
44	Maryland	58,853
45	District of Columbia	52,437
46	New Hampshire	48,582
47	Washington	41,721
48	Puerto Rico	40,032
49	Oregon	33,992
50	Maine	29,417
51	Vermont	19,364
52	Hawaii	18,271

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Texas	752
2	South Carolina	665
3	New York	566
4	Oklahoma	560
5	Arizona	534
6	Delaware	534
7	New Jersey	490
8	Kentucky	484
9	Rhode Island	477
10	Massachusetts	443
11	Florida	442
12	North Carolina	440
13	Arkansas	434
14	California	414
15	Georgia	406
16	Virginia	379
17	Connecticut	367
18	West Virginia	364
19	Alabama	358
20	Mississippi	340
21	Utah	337
22	Tennessee	327
23	Pennsylvania	313
24	Louisiana	309
25	New Hampshire	299
26	Ohio	299
27	Nevada	294
28	Indiana	285
29	District of Columbia	280
30	New Mexico	278
31	Maryland	276
32	Iowa	263
33	Montana	256
34	Nebraska	240
35	Vermont	227
36	Kansas	224
37	Illinois	212
38	Wyoming	211
39	Colorado	209
40	Wisconsin	202
41	Maine	181
42	Missouri	176
43	Alaska	175
44	Minnesota	164
45	Washington	160
46	Idaho	150
47	South Dakota	135
48	Michigan	134
49	Puerto Rico	132
50	Oregon	129
51	North Dakota	98
52	Hawaii	63

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,422
2	New York	2,228
3	Massachusetts	2,119
4	Rhode Island	2,051
5	Mississippi	2,034
6	South Dakota	2,009
7	Connecticut	1,996
8	Louisiana	1,917
9	North Dakota	1,898
10	Arizona	1,803
11	Pennsylvania	1,698
12	Illinois	1,678
13	Arkansas	1,622
14	New Mexico	1,572
15	Alabama	1,567
16	Iowa	1,554
17	Michigan	1,554
18	Indiana	1,483
19	South Carolina	1,414
20	Tennessee	1,414
21	Nevada	1,389
22	Kansas	1,307
23	Georgia	1,301
24	District of Columbia	1,297
25	Texas	1,290
26	Florida	1,242
27	Maryland	1,183
28	Missouri	1,170
29	Montana	1,155
30	West Virginia	1,131
31	Delaware	1,130
32	Minnesota	1,111
33	Wisconsin	1,105
34	California	1,046
35	Nebraska	1,042
36	Wyoming	1,029
37	Colorado	996
38	Idaho	971
39	Ohio	960
40	Oklahoma	900
41	North Carolina	895
42	Kentucky	894
43	New Hampshire	778
44	Virginia	758
45	Washington	578
46	Puerto Rico	574
47	Utah	520
48	Oregon	469
49	Maine	442
50	Alaska	345
51	Hawaii	287
52	Vermont	280

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Iowa	34
2	South Carolina	22
3	Tennessee	13
4	California	9
5	New York	9
6	Alabama	8
7	Georgia	8
8	Mississippi	8
9	Arkansas	7
10	Delaware	7
11	Louisiana	7
12	Massachusetts	7
13	New Mexico	7
14	Oklahoma	7
15	Connecticut	6
16	Florida	6
17	Kentucky	6
18	Maine	6
19	Nevada	6
20	Texas	6
21	New Hampshire	5
22	North Carolina	5
23	Pennsylvania	5
24	Rhode Island	5
25	District of Columbia	4
26	Maryland	4
27	Michigan	4
28	New Jersey	4
29	Ohio	4
30	West Virginia	4
31	Arizona	3
32	Illinois	3
33	Indiana	3
34	Kansas	3
35	South Dakota	3
36	Virginia	3
37	Colorado	2
38	Minnesota	2
39	Montana	2
40	Puerto Rico	2
41	Wisconsin	2
42	Idaho	1
43	Missouri	1
44	Nebraska	1
45	North Dakota	1
46	Oregon	1
47	Utah	1
48	Vermont	1
49	Washington	1
50	Alaska	0
51	Hawaii	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	299,621	1	99
Chattahoochee	Georgia	245,530	2	99
Dewey	South Dakota	235,743	3	99
Lake	Tennessee	235,319	4	99
Lincoln	Arkansas	234,490	5	99
Davidson	Tennessee	122,320	229	92
Richland	South Carolina	86,625	1349	57
York	South Carolina	79,586	1709	45
Orange	California	77,789	1800	42
Pierce	Washington	39,026	2896	7

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	8,346	1	99
Jerauld	South Dakota	7,948	2	99
Buffalo	South Dakota	6,626	3	99
Dickey	North Dakota	6,568	4	99
Gregory	South Dakota	6,452	5	99
Davidson	Tennessee	1,069	1896	39
Richland	South Carolina	1,049	1935	38
Orange	California	979	2029	35
York	South Carolina	915	2117	32
Pierce	Washington	518	2653	15

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.